Getting ready for a rapid prototype of your proof-of-value
In the world of enterprise architecture for data analytics, there are no clear standards. The design of an architecture depends on the data science problem you're addressing for your business.
This chapter introduces high-level requirements that you might need to consider for your enterprise architecture for big data.
There's also a summary of the most widely adopted tools for enterprise analytics, including RapidMiner, KNIME, Google Analytics, IBM Watson, and Microsoft Revolution R Enterprise. The end of this chapter presents the fundamentals of getting ready to build a rapid prototype for your predictive analytics efforts for your organization.
Enterprise Architecture for Big Data
In perspective, the goal for designing an architecture for data analytics comes down to building a framework for capturing, sorting, and analyzing big data for the purpose of discovering actionable results, as shown in Figure 17-1.
FIGURE 17-1: Thinking of the architecture that will transform big data into actionable results.
There is no one correct way to design the architectural environment for big data analytics. However, most designs need to meet the following requirements to support the ...
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